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Exam Certified Machine Learning Professional topic 1 question 48 discussion

Actual exam question from Databricks's Certified Machine Learning Professional
Question #: 48
Topic #: 1
[All Certified Machine Learning Professional Questions]

A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

  • A. mlflow.register_model
  • B. MlflowClient.update_registered_model
  • C. mlflow.add_model_version
  • D. MlflowClient.get_model_version
  • E. The machine learning engineer needs to create an entirely new MLflow Model Registry model
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Suggested Answer: A 🗳️
by BokNinja at Dec. 19, 2023, 1:39 a.m.

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saha3333
4 months, 1 week ago
Selected Answer: A
it automatically add a new version
upvoted 1 times
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Mircuz
9 months, 1 week ago
Selected Answer: A
update_registered_model allows you to update model description, the right choice should be A
upvoted 1 times
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64934ca
9 months, 2 weeks ago
Strike that, it's A...we're registering a new model, not updating the existing.
upvoted 1 times
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64934ca
9 months, 2 weeks ago
Selected Answer: B
Because the model name already exists, meaning it's registered already, and it's being refreshed: per docs: Updates metadata for RegisteredModel entity. Input field description should be non-None. Backend raises exception if a registered model with given name does not exist.
upvoted 1 times
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yunyunqian
10 months, 2 weeks ago
I think the model is already registered, the answer should be B
upvoted 1 times
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hugodscarvalho
1 year, 2 months ago
Selected Answer: A
mlflow.register_model will create a new model version in the model registry for the model files specified by model_uri. Doc: https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.register_model
upvoted 1 times
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BokNinja
1 year, 4 months ago
Answer is A import mlflow model_uri = "runs:/d16076a3ec534311817565e6527539c0/model" model_details = mlflow.register_model( model_uri=model_uri, name="project" )
upvoted 1 times
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